Apart from the randomForest package, the party package also provides an implementation of random forest. In the following steps, we illustrate how to use the cforest function within the party package to perform classifications:

1. First, install and load the party package:

        > install.packages("party")
        > library(party) 

2. You can then use the cforest function to fit the classification model:

        > churn.cforest = cforest(churn ~ ., data = trainset, con-
        trols=cforest_unbiased(ntree=1000, mtry=5))
        > churn.cforest
        Output
        Random Forest using Conditional Inference Trees

        Number of trees: 1000 

        Response: churn 
        Inputs: international_plan, voice_mail_plan, num-
        ber_vmail_messages, total_day_minutes, total_day_calls, to-
        tal_day_charge, total_eve_minutes,
        total_eve_calls, to-tal_eve_charge, total_night_minutes,
        total_night_calls, to-tal_night_charge, total_intl_minutes,
        total_intl_calls, to-tal_intl_charge, number_customer_service_calls 
        Number of observations: 2315 

3. You can make predictions based on the built model and the testing dataset:

        > churn.cforest.prediction = predict(churn.cforest, testset,
        OOB=TRUE, type = "response")

4. Finally, obtain the classification table from the predicted labels and the labels of the testing dataset:

        > table(churn.cforest.prediction, testset$churn)
        Output
        churn.cforest.prediction yes  no
                             yes  91   3
                             no   50 874